Which guests should we target?
Combine behaviour, value, property history and preferences to prioritise the audience for a campaign or offer.
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GN Hospitality Decision Pack · Hospitality Decision Intelligence
Turn guest data into the next commercial decision.
For revenue and marketing teams deciding which guests to target with which offers. Use CRM, reservation, stay and spend data to decide which guests to target, what may increase ancillary revenue, where demand is moving and which audience should receive which action.
Decisions this pack supports
Make guest intelligence operational.
Where can ancillary spend increase?
Identify guest groups, moments and offers with the strongest potential to increase onsite or pre-arrival value.
Where is demand moving, and what should we change?
Forecast demand and booking pace at the property, segment or period level, then decide where pricing, discount availability, staffing or commercial effort should change. See forecasting vs optimisation.
Who is most likely to return?
Use stay history and customer value signals to focus retention and reactivation effort.
From today's process to the next decision
Choose the guest offer and revenue action for this period.
How teams make the decision
Teams use booking and occupancy reports, CRM filters and revenue-management recommendations to plan campaigns and offers.
What this leaves unresolved
An occupancy forecast or broad guest segment leaves the audience, offer and timing to the team. Extra discounts or promotional spend can be wasted when demand is already strong.
What the GN Pack adds
Combine guest behaviour, value and booking pace to prioritise audiences and compare offer or revenue actions under property, capacity and pricing rules.
What your team receives
An actionable guest audience, offer priority or pricing and demand recommendation by property or period, with evidence and the rules used.
See the pack in action
From your business data to a decision you can act on.
Explore what a director or manager could receive. Choose a model, see its output, then compare practical actions with costs and constraints.
Illustrative interactive demoAll figures and businesses are fictional. These are examples of potential deliverables, not actual customer results, guarantees or live predictions.
HOSPITALITY / EXAMPLE DECISION WORKSPACE
Which guests should receive an offer?
Bring guest, reservation and demand signals together to recommend an economically sensible action.
01 / Explore a model output
EXAMPLE OUTPUT 01 / 05
426 guests
Family and wellness audience
What we found
A guest segment shows longer-stay patterns and interest in family and wellness experiences.
Recommended next move →
Create an audience for a suitable autumn campaign.
Decision-relevant output426 eligible guests
02 / Compare possible decisions
Which retention offer should we send?
Switch between the options and compare expected contribution after intervention costs. Numbers use fixed fictional assumptions.
Gross incremental contribution+€4,040
Intervention cost€840
Net incremental contribution+€3,200
Selected action
Targeted spa credit
Offer selected guests a €35 spa benefit for softer stay dates.
Estimated gross incremental contribution €4,040 less €840 maximum spa-credit cost.
Scope: 24 guestsFeasibility: Within stated scenario rules
Operating constraints considered
See what this pack could recommend for your business →- Available rooms on target dates
- Maximum offer spending and contact eligibility
- Only estimated incremental response counts toward benefit
Read every example output and decision alternative
Guest Segmentation: 426 guests — Family and wellness audience
A guest segment shows longer-stay patterns and interest in family and wellness experiences. Create an audience for a suitable autumn campaign. 426 eligible guests.
Guest Value / Propensity: 103 guests — High risk of not returning
Thirty-eight of the at-risk guests have high predicted future value. Examine whether a retention intervention is economically justified. 38 priority guests.
Demand Forecasting: 83% — Forecast September occupancy
September room revenue is forecast at €1.24M; weekend occupancy is 91% versus 67% on softer weekdays. Focus campaigns on softer periods, not dates already filling naturally. €1.24M modelled room revenue.
Offer / Ancillary Uplift: 24 guests — Likely incremental responders
A small group may change booking behaviour because of a specific spa-credit offer. Test the offer with an appropriate holdout group. 24 treatment candidates.
Commercial Optimisation: €3,200 — Expected incremental contribution
A targeted campaign fits offer and capacity limits better than a broad room discount. Prioritise selected guests for soft-demand dates. +€3,200 modelled net contribution.
Decision options
Do not send any targeted offers. No intervention cost and no incremental uplift. Scope: 0 guests.
Offer selected guests a €35 spa benefit for softer stay dates. Estimated gross incremental contribution €4,040 less €840 maximum spa-credit cost. Scope: 24 guests.
Offer a 15% room discount to 103 at-risk guests. More widespread contact but substantial discount leakage to guests who may return anyway. Scope: 103 guests.
Models inside this pack
One product bundle around a recurring business decision.
GN Hospitality combines the models, business logic and constraints needed to move from your operating data to a decision-ready recommendation.
DECISION PACKGN Hospitality
Built aroundCRM · reservations · stays · spend · campaigns
Guest SegmentationBuilds behavioural, demographic, property and interest segments from guest and stay data.
Guest Value / PropensityEstimates value, return likelihood, offer response or ancillary-purchase propensity.
Demand ForecastingForecasts property, period or segment demand using reservations, booking pace and stay history.
Offer / Ancillary UpliftEstimates which guest groups are more likely to change behaviour because of an intervention.
Commercial OptimisationRanks audiences and actions under capacity, seasonality, offer and commercial constraints.
OUTPUTDecision support your team can use
Models are combined only where the decision needs them. The pack returns a business-ready action with supporting evidence and expected impact.
- Audience
- offer
- demand
- revenue action
- Expected impact and supporting evidence
- Configured around your real operating constraints
Guest Segmentation
Builds behavioural, demographic, property and interest segments from guest and stay data.
Which audiences should be treated differently in campaigns and guest journeys.
Guest Value / Propensity
Estimates value, return likelihood, offer response or ancillary-purchase propensity.
Which guests deserve priority and which offer is commercially sensible.
Demand Forecasting
Forecasts property, period or segment demand using reservations, booking pace and stay history.
Where pricing, discount availability, staffing, inventory or marketing plans need to change.
Offer / Ancillary Uplift
Estimates which guest groups are more likely to change behaviour because of an intervention.
Who should receive a specific upgrade, ancillary or campaign action.
Commercial Optimisation
Ranks audiences and actions under capacity, seasonality, offer and commercial constraints.
Which guest or revenue action should happen next.
Pack anatomy
One repeatable path from guest data to action.
Reusable audience and decision workflows, configured around the property, guest journey and commercial objectives.
Your guest and stay data
CRM, reservations, stays, property history, spend, campaigns, channels, preferences and other available commercial signals.
Segmentation + predictive models
Use segmentation, customer value, propensity, forecasting and causal methods where the decision requires them.
Guest and commercial constraints
Define the offer, audience rules, capacity, seasonality, property context and business objectives that make the action useful.
A guest or revenue action
Return the prioritised audience, offer opportunity, demand signal or intervention to the marketing or commercial workflow.
Demand forecast → pricing and revenue action
Do not stop at the occupancy forecast.
Graphite Note can use bookings, booking pace, occupancy, ADR, cancellations, lead time, room or property type, seasonality, events and available market context to identify where the commercial plan should change.
The output is a decision such as reducing discount availability for a strong period, shifting campaign spend toward a weak period, prioritising higher-value segments or flagging where extra promotion is unnecessary.
Example recommended move
Demand is pacing above expectation.
Reduce discount availability for selected room categories rather than adding promotional spend, subject to the property's pricing rules and commercial constraints.
Evidence at scale
Millions of guest records. One structured audience layer.
A published Graphite Note case study describes a multi-property resort group with more than 3.8 million guest accounts. The engagement produced four interoperable segmentation dimensions from raw CRM exports.
Read the full case studyPublished case
3.8M+ guest accounts segmented
Behavioural, property, demographic and interest dimensions created a reusable audience layer for commercial activation.
Direct answers
Questions about the GN Hospitality Decision Pack.
What is the GN Hospitality Decision Pack?
It is a packaged Decision Intelligence solution for recurring hospitality decisions around guest value, segmentation, targeting, ancillary revenue, repeat behaviour and demand.
What data does it use?
Typical inputs include CRM, reservations, stays, property history, guest spend, campaigns, channels and available preference signals.
Do we need an internal data-science team?
No. Graphite Note can provide the modelling and decision layer around the data and workflows you already have, while your commercial teams remain owners of the business decision.
Does Graphite Note replace our PMS, CRS or revenue-management system?
No. Graphite Note can complement existing PMS, CRS or revenue-management systems by adding predictive, causal and optimisation models around specific decisions and returning recommended actions into the workflow.
What comes out?
Examples include actionable guest segments, prioritised audiences, demand forecasts, ancillary-revenue opportunities, pricing or demand actions and other recommended commercial moves.
Decision IntelligenceFrom signal and prediction to a governed business action.AutoMLAutomate repetitive model-building work without losing business context.Causal Machine LearningSeparate drivers from correlations and estimate intervention effects.Predictive AnalyticsForecast demand, score customers and anticipate business outcomes.Decision OptimisationCompare feasible actions and rank them by expected business impact.Forecasting vs OptimisationSee where prediction ends and the business decision begins.Decision APIDeliver recommendations, expected impact and evidence into workflows.Embedded Decision IntelligenceAdd a decision layer to ERP, CRM, BI, workflow and vertical software.Build vs BuyA practical framework for deciding whether to build, buy or embed.
Bring the guest or revenue decision you want to improve.
We will scope the pack around the data you already have and the action your team can actually take.